Harnessing Machine Learning for Smart Agriculture: Integrating Data-Driven Approaches for Crop Improvement
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V4I3P101Keywords:
Machine Learning, Smart Agriculture, Crop Yield Prediction, Precision Farming, Deep Learning, Data Analytics, IoT Sensors, Supervised Learning, Drone Imagery, SustainabilityAbstract
Smart agriculture, also known as precision agriculture, leverages advanced technologies to optimize agricultural practices and enhance crop yields while minimizing environmental impact. Machine learning (ML) is a key component of smart agriculture, enabling data-driven decision-making through the analysis of vast amounts of agricultural data. This paper explores the integration of machine learning techniques in smart agriculture, focusing on crop improvement. We discuss the various ML algorithms and methodologies used in this domain, the data sources and preprocessing techniques, and the practical applications of these technologies. Additionally, we present case studies and empirical results to illustrate the effectiveness of ML in improving crop yields and sustainability. The paper concludes with a discussion on the challenges and future directions in the field
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